Next-Generation Price IQ®: Empower Sales Teams with Unprecedented Price Transparency
By Zilliant
Apr 22, 2022
Table of Contents
Read on for a breakdown of how price optimization benefits B2B sales teams, and how Zilliant’s Next-Generation Price IQ® empowers sales reps even further with unprecedented price transparency.
Why Price Optimization Software is Critical
Setting rational, market-aligned prices that make sense for each unique selling circumstance within a B2B company is a massive challenge. The many types of prices – including list, matrices or tiers, customer-specific agreements, spot negotiations, and overrides – are all connected, which creates pricing complexity for sales teams. This complexity is exacerbated by large customer and product counts and complicated product configurations. Making matters worse, inflation has become the new business reality, along with supply chain turmoil, increased competition, and dramatic swings in both demand and inventory availability. Therefore, it should be no surprise that maintaining, updating, and publishing price changes with manual and spreadsheet-driven tools is a near impossibility.
B2B companies need a faster, more powerful approach to pricing that maintains market alignment while generating prices that are easily explainable and defensible. That’s why price optimization software, which can account for all the factors that drive prices and rationally align price/customer/order/product relationships simultaneously, is so critical to success in today’s business landscape. Price optimization software can statistically measure what’s driving market prices, all while enforcing the necessary guardrails and producing price guidance for all the different ways price is expressed. By leveraging the power of data science and price optimization software, a B2B business can dramatically impact profitability while improving responsiveness to market dynamics.
The Benefits of Price Optimization for B2B Sales Teams
Optimized prices provide market-aligned pricing guidance to sales reps that is specific to each unique selling circumstance. With inline analytics right in their CRM, CPQ, or other quoting tools explaining the rationale behind the optimized pricing, price optimization boosts sales reps’ confidence in price recommendations and enables them to spend more time selling.
Finding that optimized price, however, begins with an analysis of past transaction data, which ensures that the optimal price is never out of scope for the product and selling circumstance (order size, customer type, geography, etc.). Essentially, transactional data-driven and intelligent pricing takes the wisdom of sales reps, applies math and science, and converts it into actionable price guidance for those same sales reps. In the end, it’s the experience that the sales team has accumulated in the transaction history that helps drive prices.
In addition to providing market-aligned prices, optimized pricing helps B2B sales teams in three ways:
Remedy Irrational Pricing
For many B2B companies, pricing decisions are largely decentralized, and it’s not uncommon for historical prices to be out of alignment with respect to rational price relationship expectations. For example, small customers get better pricing than large customers, all else being equal, or premium products sell for less than mid-tier products, all else being equal. Pricing that aligns with customer relationships, order size, and product value can quickly become irrational. This puts sales reps’ confidence in prices and customer satisfaction in jeopardy.
The solution to this challenge is enabling constraint-based price optimization. A pricing model built with constraint-based optimization can simultaneously account for business rules and customer or price relationship requirements. This helps to avoid the hazards of conflicting rules.
Reduce Quote Turnaround Time with Price Guidance
For many B2B pricing projects, companies provide prices to only a handful of pricing professionals and then expect sales reps to route their thousands of exception requests through the pricing team for approval. This often results in a huge backlog of pricing requests for pricing teams that creates a bottleneck and leads to slower quote turnaround times and sales team frustrations. The better and more effective approach is to give the price guidance directly to the sales reps. Companies that take this approach see substantial improvements in margin rate, margin dollars, and price realization while speeding up quote time. When starting with the market-aligned price, exception requests drastically decline.
Turn “B” and “C” Players into “A” Players
If your company is like most organizations, your sales team consists of primarily “B” and “C” players along with a handful of “A” performers, or those reps that consistently meet or exceed quota and do so with profitable sales. But what would the impact be on a company’s bottom line if all the B and C players could sell at the same level?
The key to achieving this kind of impact is enabling all reps to know which price to charge in every selling circumstance. If those in the B and C tier could take every deal they are negotiating and have the prices reviewed by the A players on the team, would that make a difference in the kinds of prices the B and C performers were able to achieve? In an abstract sense, this is what data-driven optimal pricing is really doing. It sees what the A players can achieve under many different selling circumstances (i.e., for each price segment), and it recommends optimal price points for the B and C players to follow.
Next-Generation Price IQ®: Unprecedented Price Transparency for Sales Teams
As previously stated, Next-Generation Price IQ is a massive leap forward for our market-leading B2B price optimization solution. Not only does it deliver huge advances in performance, with a 10x improvement in AI-based optimization speeds, but one of the most significant improvements is the new crystal box optimization that provides substantial visibility into how prices were derived.
One thing we’ve learned from our customers is that if sales reps don't understand how a price was generated, they're unlikely to use it. Simply giving a sales rep a price and telling them to use it won’t achieve the adoption necessary to obtain the outcome of a desired pricing strategy. This is especially true if the price they were given is wildly different from the price they thought was correct based on gut feel and intuition.
When B2B companies use artificial intelligence or machine learning to optimize prices, how those prices are derived must be crystal clear, easily explainable, and defensible. With next-generation Price IQ’s crystal box optimization, a pricing analyst can gain valuable insights into all the various factors that go into deriving optimized prices. That means visibility into the inputs and parameters to deconstruct how prices changed throughout the various steps in the optimization process. This is crucial because as these pricing analysts are interacting with the sales reps, and they are able to clearly describe how the price guidance was derived and explain the pricing rationale in detail. Additionally, when presented with a target price, or negotiation guidance, it is often helpful to provide some additional context to the sales rep showing why that price is relevant in that selling circumstance. By giving sales reps embedded pricing analytics in their quoting or order entry tool, they can feel confident in their pricing decision because they understand the rationale as to how one customer’s recommended price makes sense relative to peers in that same pricing segment.
Ultimately, sales reps need to trust the prices they’re using, and next-generation Price IQ offers a transparent, proven, and pragmatic pricing solution using data science and AI that’s both sophisticated and flexible. With next-generation’s Price IQ’s great advances in performance, speed, and pricing transparency, Zilliant is helping embroiled B2B companies navigate a supply-constrained, pandemic-disrupted, and inflationary time to meet and exceed their margin and revenue goals.
